The network is trained in an end-to-end manner. Specifically, we train the network with the Adam optimizer, where \(\beta _{1}\)  and \(\beta _{2}\)  take 0.9 and 0.999, respectively. The initial learning rate is set to 0.0001. We employ a mini-batch size of 16 to train our network. The hazy images are randomly cropped to \(224 \times 224\)  and used as the input to our network during training phase. Moreover, we train the network for 120 epochs in the ITS [1]}, TrainA-DCPDN [2]} and NH-HAZE decay the learning rate by 0.5 times after 40 epochs. As for OTS-\(\beta \) , considering the large number of data sets, we train 20 epochs and multiply the learning rate with 0.5 every 10 epochs. Moreover, for the Foggy Cityscapes-DBF [3]}, we train 20 epochs with the learning rate of 0.0001. The training is carried out on a PC with NVIDIA GeForce GTX TITAN XP.
